- A
Frequency penalty
Why wrong: Frequency penalty discourages the model from repeating the same words, which can reduce repetition but does not directly increase overall randomness.
- B
Top_p (nucleus sampling)
Why wrong: Top_p controls the cumulative probability of token choices; lowering it can make output more focused, increasing it can add diversity but it's not the primary parameter for randomness.
- C
Temperature
Temperature directly controls the level of randomness; increasing it makes the model more likely to choose less probable tokens, leading to more creative and varied outputs.
- D
Presence penalty
Why wrong: Presence penalty encourages the model to talk about new topics, which can add diversity in content but does not primarily control randomness in token selection.
Quick Answer
The answer is to increase the temperature parameter. Temperature directly controls the randomness of token selection within the model’s probability distribution; a higher temperature flattens this curve, making lower-probability tokens more likely to be chosen, which introduces greater diversity and creativity in the generated text. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of how to adjust model behavior for specific tasks, often appearing in scenario-based questions about generating varied outputs versus focused, deterministic responses. A common trap is confusing temperature with top_p, which also influences randomness but through nucleus sampling rather than scaling probabilities. For a quick memory tip, think of temperature like a thermostat for creativity: turn it up to get more surprising ideas, or keep it low for safe, predictable results.
AI-900 Practice Question: Describe features of generative AI workloads on Azure
This AI-900 practice question tests your understanding of describe features of generative ai workloads on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A marketing team uses Azure OpenAI Service to generate headline ideas for a campaign. They find the generated headlines are often too similar and lack creativity. Which parameter should they increase to introduce more randomness in the generated text?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Temperature
Option C (Temperature) is correct because temperature controls the randomness of token selection in the model's probability distribution. Increasing temperature (e.g., from 0.7 to 1.0) flattens the probability curve, making lower-probability tokens more likely to be chosen, which introduces more diversity and creativity in the generated headlines.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Frequency penalty
Why it's wrong here
Frequency penalty discourages the model from repeating the same words, which can reduce repetition but does not directly increase overall randomness.
- ✗
Top_p (nucleus sampling)
Why it's wrong here
Top_p controls the cumulative probability of token choices; lowering it can make output more focused, increasing it can add diversity but it's not the primary parameter for randomness.
- ✓
Temperature
Why this is correct
Temperature directly controls the level of randomness; increasing it makes the model more likely to choose less probable tokens, leading to more creative and varied outputs.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Presence penalty
Why it's wrong here
Presence penalty encourages the model to talk about new topics, which can add diversity in content but does not primarily control randomness in token selection.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse temperature with frequency or presence penalties, thinking that penalizing repetition (frequency penalty) will increase creativity, when in fact temperature directly controls the randomness of token selection, which is the key to generating more diverse and creative text.
Trap categories for this question
Command / output trap
Top_p controls the cumulative probability of token choices; lowering it can make output more focused, increasing it can add diversity but it's not the primary parameter for randomness.
Detailed technical explanation
How to think about this question
Temperature works by scaling the logits (raw scores) before applying the softmax function: logits = logits / temperature. A temperature of 1.0 leaves the distribution unchanged, while values above 1.0 (e.g., 1.5) make the distribution more uniform, increasing the chance of selecting less likely tokens. In practice, for creative tasks like headline generation, a temperature between 0.8 and 1.2 is often used, while lower values (e.g., 0.2) are preferred for factual or deterministic outputs.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe features of generative AI workloads on Azure — This question tests Describe features of generative AI workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Temperature — Option C (Temperature) is correct because temperature controls the randomness of token selection in the model's probability distribution. Increasing temperature (e.g., from 0.7 to 1.0) flattens the probability curve, making lower-probability tokens more likely to be chosen, which introduces more diversity and creativity in the generated headlines.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
3 more ways this is tested on AI-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A writer uses Azure OpenAI Service to generate story ideas. The current configuration uses a temperature setting of 0, causing the model to produce identical outputs for the same prompt. The writer wants more creative and diverse outputs. Which parameter should be increased?
medium- A.max_tokens
- ✓ B.temperature
- C.top_p
- D.frequency_penalty
Why B: Temperature controls the randomness of the model's output. A temperature of 0 makes the model deterministic, always choosing the most likely next token, which leads to identical outputs for the same prompt. Increasing the temperature (e.g., to 0.7 or higher) introduces more randomness, allowing the model to sample from less likely tokens and produce more creative, diverse story ideas.
Variation 2. A marketing team uses Azure OpenAI Service to generate multiple variations of a product description from a single prompt. They want the generated descriptions to be more creative and diverse, rather than repetitive. Which parameter should they increase to achieve this?
easy- ✓ A.Temperature
- B.Max tokens
- C.Top probability
- D.Frequency penalty
Why A: Increasing the Temperature parameter makes the model more creative and diverse by raising the randomness of token selection. At higher temperatures (e.g., 0.8–1.0), the model assigns more weight to less probable tokens, producing varied and unexpected outputs. This directly addresses the need for diverse product descriptions rather than repetitive ones.
Variation 3. A marketing team uses Azure OpenAI Service to generate product descriptions. They have a base description and want the model to produce multiple variations with different tones, such as formal, playful, and technical, while still being factually accurate. Which parameter should they adjust to control the randomness and diversity of the output?
medium- ✓ A.temperature
- B.max_tokens
- C.top_p
- D.frequency_penalty
Why A: Temperature controls the randomness of the model's output by scaling the logits before applying the softmax function. A higher temperature (e.g., 0.8) increases diversity and creativity, while a lower temperature (e.g., 0.2) makes the output more deterministic and focused. For generating product descriptions with different tones while maintaining factual accuracy, adjusting temperature is the correct approach.
Last reviewed: Jun 11, 2026
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